Childhood deprivations predict late-life cognitive impairment among older adults in India

Large population-based studies on the associations of childhood factors with late-life cognition are lacking in many low and middle income countries including India. In this study, we assessed the prevalence of late-life cognitive impairment and examined the associations of childhood socioeconomic status (SES) and health conditions with cognitive impairment among older adults in India. Data for this study were derived from the Longitudinal Ageing Study in India conducted in 2017–18. The effective sample size was 31,464 older adults aged 60 years and above. Cognitive functioning was measured through five global domains (memory, orientation, arithmetic function, executive function, and object naming). The overall score ranged between 0 and 43, and the score was reversed indicating cognitive impairment. Descriptive statistics along with mean scores of cognitive impairment were presented. Additionally, moderated multivariable linear regression models were employed to examine the association between explanatory variables, including childhood SES and health conditions and late-life cognitive impairment. The mean score of cognitive functioning among the study participants was 21.72 (CI 2.64–21.80). About 15% of older adults had poor health conditions, and 44% had lower financial status during their childhood. Older adults who had a fair health during their childhood were more likely to suffer from cognitive impairment in comparison to older adults who had good health during their childhood (Coef: 0.60; CI 0.39, 0.81). In comparison to older adults who had good childhood financial status, those who had poor childhood financial status were more likely to suffer from cognitive impairment (Coef: 0.81; CI 0.56, 1.07). Older adults who had fair childhood health status and poor childhood financial status were more likely to suffer from cognitive impairment in comparison to older adults who had good childhood health and good financial status (Coef: 1.26; CI 0.86, 1.66). Social policies such as improving educational and financial resources in disadvantaged communities and socioeconomically poor children and their families, would help to enhance a better cognitive ageing and a healthy and dignified life in old age.


Methods
Data. Data for this study were derived from the recent release of the Longitudinal Ageing Study in India (LASI) wave 1. The LASI is a full-scale national survey of scientific investigation of the health, economic, and social determinants and consequences of population ageing in India, conducted in 2017-18 by the International Institute for Population Sciences (IIPS) in partnership with national and international institutions 38 . The LASI is a nationally representative survey of 72,250 individuals aged 45 and above across all states and union territories of India. The main objective of the survey is to study the health status and the social and economic well-being of older adults in India. LASI adopted a multistage stratified area probability cluster sampling design to arrive at the eventual units of observation: older adults age 45 and above and their spouses irrespective of age. The survey adopted a three-stage sampling design in rural areas and a four-stage sampling design in urban areas. In each state/Union Territory, the first stage involved the selection of Primary Sampling Units (PSUs), that is, sub-districts (Tehsils/Talukas), and the second stage involved the selection of villages in rural areas and wards in urban areas in the selected PSUs. In rural areas, households were selected from selected villages in the third stage. However, sampling in urban areas involved an additional stage. Specifically, in the third stage, one Census Enumeration Block (CEB) was randomly selected in each urban area. In the fourth stage, households were selected from this CEB. The socio-demographic and health-related information of respondents in the LASI survey was assessed using the face-to-face interviews which were conducted using computer-assisted personal interview (CAPI). The detailed methodology, with the complete information on the survey design and data collection, was published in the survey report 38 . The present study is conducted on eligible respondents aged 60 years and above. The total sample size for the present study is 31,464 (15,098 males and 16,366 females) elderly persons aged 60 years and above. Figure 1 represents the flowchart for the study sample selection. All methods were performed in accordance with the relevant guidelines and regulations.
Variable description. Outcome variable. Cognitive functioning was assessed using continuous measures of five global domains of cognition (memory, orientation, arithmetic function, executive function, and object naming), adapted from the Mini-Mental State Examination (MMSE) 39 , and the cognitive module of the Health and Retirement Study, the China Health and Retirement Longitudinal Study (CHARLS), and the Mexican Health and Aging Study (MHAS) 40,41 . Memory was measured using immediate word recall and delayed word recall. Orientation was measured using time and place measures. The arithmetic function was measured through backward counting, a serial seven subtraction task and a task involving two computations 38,40 . Additionally, paper folding (folding a piece of paper according to instructions), pentagon drawing (drawing intersecting circles) and object naming methods were followed to measure the cognitive functions among older adults 41 (Cronbach's alpha: 0.70). The overall score of composite index of cognitive function ranged between 0 and 43, and a higher Mediating factors. As per the above-mentioned literature, the following factors which were shown to potentially mediate the associations between childhood factors and late-life cognition were included in the current analysis. Social participation was measured through the question-"Are you a member of any of the organizations, religious groups, clubs, or societies?" and the response was coded as no and yes. Physical activity was categorized as frequent (every day), rare (more than once a week, once a week, one to three times in a month), and never. The question through which physical activity was assessed was "How often do you take part in sports or vigorous activities, such as running or jogging, swimming, going to a health centre or gym, cycling, or digging with a spade or shovel, heavy lifting, chopping, farm work, fast bicycling, cycling with loads?" 43 . If the older adult was ill-treated in the last 1 year, then it was coded as "yes"; otherwise "no".
The probable major depression among older adults with symptoms of dysphoria, was calculated using the CIDI-SF (Short Form Composite International Diagnostic Interview) with a score of 3 or more indicating "diagnosed with depression" 44 . The Cronbach's alpha value for the CIDI-SF scale was 0.668. This scale estimates a probable psychiatric diagnosis of major depression and has been validated in field settings and widely used in population-based health surveys 38 . Self-rated health was coded as good which includes excellent, very good, and good whereas poor includes fair and poor 45 . Difficulty in activities of daily living (ADL) was coded as no and yes. The Cronbach's alpha value for ADL scale was 0.869. ADL is a term used to refer to normal daily self-care activities (such as movement in bed, changing position from sitting to standing, feeding, bathing, dressing, grooming, personal hygiene, etc.). Difficulty in instrumental ADL (IADL) was coded as no and yes 46 . The Cronbach's alpha value for IADL scale was 0.879. These include activities that are not necessarily related to the fundamental functioning of a person, but they let an individual live independently in a community. Morbidity was coded as no morbidity, 1 and 2 + 47 . The variable morbidity was created using the data on chronic diseases which include hypertension, chronic heart diseases, stroke, any chronic lung disease, diabetes, cancer or malignant tumor, any bone/joint disease, neurological/psychiatric disease, or high cholesterol 47 .
Control variables. Several socio-demographic variables were controlled in the analysis. They include, age, which was recoded as young old (60-69 years), old-old (70-79 years), and oldest-old (80 + years); sex, which was recoded as male and female; educational status (equivalent to the International Standard Classification of Education (ISCED) categories) 48 , which was recoded as no education/primary not completed, primary (ISCED 1), secondary (ISCED 2, 3 and 4) and higher; working status, which was recoded as never worked, currently working, currently not working and retired; marital status, which was categorized as currently married, widowed, and others (divorced/separated/never married); and living arrangement, which was categorized as living alone, living with a spouse, living with spouse and children, and living with others. www.nature.com/scientificreports/ Further, the monthly per capita consumption expenditure (MPCE) quintile was assessed using household consumption data. Sets of 11 and 29 questions on the expenditures on food and non-food items, respectively, were used to canvas the sample households. Food expenditure was collected based on a reference period of seven days, and non-food expenditure was collected based on reference periods of 30 days and 365 days. Food and non-food expenditures have been standardized to the 30-day reference period. The MPCE is computed and used as the summary measure of consumption 38 . The variable was divided into five quintiles i.e., from poorest to richest. Religion was coded as Hindu, Muslim, Christian, and Others. Caste was recoded as Scheduled Tribe (ST), Scheduled Caste (SC), Other Backward Classes (OBCs), and others. The ST refers to a large number of aboriginal ethnic groups or the indigenous population in the country. The SC includes the population that is socially segregated and financially/economically weak by their low status as per the Hindu caste hierarchy. The STs and SCs are among the most disadvantaged and discriminated socio-economic groups as per Government of India official classification. The OBC is the group of people who were identified as "educationally, economically and socially backwards". The OBCs are considered low in the traditional caste hierarchy but are higher in status than the STs/SCs. The "other" caste category is identified as those who are having higher social status, mostly belonging to the upper caste Hindus 49 . The place of residence was coded as rural and urban. The regions of India were coded as North, Central, East, Northeast, West, and South. Statistical analysis. Descriptive statistics along with mean (95% confidence interval) was presented in the study. Additionally, moderated multiple linear regression analysis 50 was used to examine the association between the outcome variable (cognitive impairment) with other explanatory variables. The estimates were presented in the form of adjusted coefficients calculated at 95% confidence interval (CI). Additionally, standard beta coefficients were presented in the results. The regression diagnostics for heteroscedasticity 51 , multicollinearity 52 , and outliers were performed via computation of variance inflation factors (VIFs) and visual inspection of residual plots for the regression models. The complex survey design effects were adjusted by using STATA svyset and svy commands. The whole statistical analyses were performed by using Stata version 14 53 . Model-1 provides the estimates adjusted for all the mediating and control variables considered in the study. Model-2 represents the adjusted estimates of interaction effects of childhood health (good fair, and poor) and childhood financial status (good, average, poor) with cognitive impairment among older adults. Model-3 represents the estimates from the stratified analysis (categorical results) of childhood health and childhood financial status.

Ethics approval and consent to participate. The Indian Council of Medical Research (ICMR), Delhi
and Institutional Review boards (IRBs) of all partner institutions extended the necessary guidance and ethical approval for conducting the LASI survey. The partner institutions included IIPS, Mumbai; Harvard T.H. Chan School of Public Health (HSPH), Boston; University of Southern California (USC), Los Angeles; ICMR-National AIDS Research Institute (NARI), Pune; and the Regional Geriatric Centres (RGCs), ministry of health and family welfare (MoHFW). Informed consent was obtained from all subjects and/or their legal guardian(s) in accordance with human subject protection protocols. Table 1 represents the socioeconomic profile of the older adults in India. It was found that about 3.7% of the older adults had poor health conditions in their childhood. Nearly 43.9% of the older adults had a poor childhood SES. Around 68.0% of the older adults had no education or their primary education was incomplete. Nearly 26.4% of the older adults were currently not working. About 36.2% of older adults were widowed, whereas about 61.6% were currently married. Nearly 5.7% of older adults lived alone, and about 20.3% lived with their spouses. About 95.5% of older adults had no social participation. Nearly 69.3% of the older adults reported that they have never done any physical activity. Almost 5.2% of the older adults were ill-treated in last 1 year. About 8.7% of the older adults were suffering from depression. Nearly 48.6% of the older adults reported having poor self-rated health. About 23.8% and 48.3% of the older adults reported having difficulty in ADL and IADL, respectively. About 23.9% of the older adults had 2 + chronic conditions. Table 2 represents the mean score of cognitive impairment by background characteristics. It was found that the mean score of cognitive impairment was higher among older adults whose childhood status was fair (mean: 22 Model-2 represents the interaction estimates of childhood health and financial status on cognitive impairment among older adults. Although the actual main effects were significant, there was no statistical significance in the interaction effects. Further, Model-3 represents the stratified analysis of childhood health and childhood financial status. Older adults who had fair childhood health and poor childhood financial status were more likely to suffer from cognitive impairment in comparison to older adults who had good childhood health and good

Discussion
It is fairly well established that early life childhood deprivation, environment and childhood health may contribute to cognitive impairment in the later life stages 54 . However, the risk of cognitive impairment in old age due to adverse socioeconomic conditions in childhood has been understudied in India compared to other developing countries. Such a study is particularly important in a resource-constrained setting where there is a need for wider efforts to reduce the prevalence of cognitive impairment among older adults and its burden on health care systems. This study used data from a large population-based ageing survey conducted in India, to advance understanding of childhood socioeconomic and health conditions as major factors in the early-life course that associate with cognitive function in later years of life. As evidence suggests, children from households with higher SES may be in a more cognitively stimulating environment in their early life resulting in more advanced brain development than their disadvantaged counterparts 55 . Such advancements in the brain in the early life course are shown to be associated with better    www.nature.com/scientificreports/ cognitive functioning in older ages 56 . Studies drawing on data from different socio-cultural settings had found that older adults, when they experienced higher levels of SES in childhood, perform better on neurocognitive tests 57 . On the other hand, it is documented that the longer people live in poor SES and health conditions, the greater would be their academic deficits and the more severe the decline in their cognitive abilities 58 . Concordantly, the present analysis provides consistent evidence that childhood SES predicts cognitive impairment in older ages. The experiences in childhood do substantially influence the health status in later life because childhood conditions predict to a great extent, the probable pathways that may lead to good or bad health. As multiple studies suggest, childhood economic resources and health determine the living and working conditions in adulthood, and those circumstances give rise to social inequalities in health 59,60 . Besides, nutritional deprivation during such important periods of early development may have negative effects on cognitive functioning in the long term 34 . Parallel to these findings, current results also suggest that compared to good childhood health, fair health condition is significantly associated with cognitive impairment among older adults. Both findings could be interpreted as evidence that childhood SES and health conditions may have a long-lasting effect on an active cognitive reserve that may have a major role in determining the rate of cognitive functioning in later years of life.

Yes
In the interaction analysis model that included a term for the interaction of childhood health and financial status, we did not find evidence for both the childhood adversities in combination increasing the significance effect on cognitive impairment. However, additional regression analysis including stratification of low childhood SES measured by a worse-off family financial status and fair childhood health conditions showed that they were statistically significant for cognitive impairment in old age. All these suggest that the effects of low childhood SES on late-life cognitive impairment were stronger for people with fair childhood health conditions than for people with good childhood health. Although fair childhood health status in the stratified estimates showed higher cognitive impairment, poor childhood health status did not show statistical significance which might be attributed to the lower sample size in the poor health category that might result in lack of statistical power in the analysis. Further longitudinal studies are warranted in developing countries like India that explore the rate of cognitive decline in old age in relation to the life course socioeconomic and health conditions. Such an investigation may further contribute to an improved understanding of the mechanisms such as lack of social and economic resources and increased illiteracy surrounding the cognitive impairment in old age and bringing interventions for early detection and prevention of cognitive impairment and related disabilities in older ages. The study also supports that the association of early life circumstances with cognitive characteristics in old age observed in high-income countries and some developing countries, including China, may extend to communitydwelling older adults in the Indian context as well.
Another particularly striking finding in our analysis was the protective effect of education on a late-life cognitive impairment that is consistent with past literature [61][62][63] , indicating that providing education as an intervention to diminish the adverse effects of poor childhood SES and health conditions on cognitive ageing. Significantly increased odds of lower education in relationship with cognitive impairment in our analysis support the findings of previous studies suggesting that the higher levels of education often lead to occupations that necessitate active cognitive involvements, which could further enhance or maintain cognitive functioning in late adulthood 62,64 . At the same time, children may lack the energy and motor skills essential to thrive in school due to poor household conditions and limited resources and thus complete fewer years of schooling, which in turn affects late-life cognition 65,66 . Hence, considering the findings of the present study, adverse household conditions could be an indicator for identifying the children at-risk who would benefit in the long term from targeted interventions on increasing their education.
The current findings revealed a significant female disadvantage in cognition and a stronger association of childhood health conditions with late-life cognitive functioning among women than men, where older women with a fair health status in childhood had higher odds of cognitive impairment in late-life compared to those with a good health status during childhood. This is consistent with previous studies 67 which suggest that women are at higher disadvantage in terms of having adverse structural, behavioural and psychosocial characteristics across the lifespan that are related to poor late-life health outcomes. Again, the current finding is similar (for childhood SES) to existing studies which showed that childhood SES was associated with old-age mental health among women but not among men in models fully adjusted with adulthood SES and risk factors 68,69 . Considering the interaction results segregated by gender, having a fair childhood health and poor childhood SES had higher odds of cognitive impairment both among men and women in comparison to having a good health and SES in childhood and the odds were greater among women. On the other hand, a study based on the data from the HRS suggested that with respect to memory function, cumulative SES from childhood to adulthood may be more important among men than women 70 , which suggests the need for future studies on the influence of cumulative exposure to life-course disadvantages on late-life health with special focus on gender aspect.
There are several limitations of the present study to be acknowledged. Foremost, the cross-sectional design of the analysis in the present study prevents bringing out any causal inferences. Further, although a global measure of cognitive impairment has the advantage of assessing overall cognition, the relationship with early life health and SES is potentially different for specific domains of cognitive function. For example, memory unlike other cognitive functions is found to be more sensitive measure of age-related cognitive deficits 71,72 . Therefore, future studies on domain-specific associations are warranted. A sensitivity analysis was conducted in the current study after excluding participants who were cognitively impaired or suspected with dementias, and the results showed no changes in the observed associations. Also, measurement error in several cognitive domains may be biasing the current results due to a higher proportion of illiterate population in India (with 68% older adults with no or uncompleted primary education in this study), and thus additional research with longitudinal and interventional designs is required to unravel this issue. Notably, educational variable and its categories in the current study are equivalent to the ISCED and allow comparisons to be made with other international studies. Similarly, studies on the validity and reliability of the measurement method of cognitive impairment are recommended. www.nature.com/scientificreports/ Indeed, it is also important to consider that some childhood conditions may affect cognitive functioning directly, and others may act indirectly through several pathways in adulthood 6,73 . Hence, understanding SES in adulthood as a risk/protective factor for later-life cognition is also essential for identifying the factors related to cognitive ability in older ages. Also, self-report of health conditions and SES in childhood may be subject to recall bias and information on receiving healthcare support/assistance for reported poor health conditions was not available which may bias the current results. This study, however, provides baseline data for understanding the ageing trajectories and the risk factors for cognitive impairment in late life. Further longitudinal studies with more follow-up information from upcoming waves of LASI surveys may add to this gap. Another major limitation is that given the predictor variables of interest in the study are self-reported, there are greater chances of recall bias, especially in the case of childhood conditions. However, the study has the credit of utilizing the large survey information of the older population, which is nationally representative and provides comprehensive measures of cognitive functioning in an ageing population.

Conclusions
The current study's findings highlight the necessity of determining whether certain developmental periods are linked to cognitive impairment later in life. Our findings also imply that governments should place a greater emphasis on closing socioeconomic resource inequalities across the lifespan, especially in childhood. Furthermore, there are various windows of opportunity for age-based interventions, with those in the early years of life shaping individuals' socioeconomic paths into later life being the most promising. As a result, social measures such as increasing educational and financial resources in disadvantaged neighbourhoods and socioeconomically poor children and their families may aid in cognitive ageing and a healthy and dignified life in old age. Without a question, socioeconomic measures aimed at improving childhood conditions are critical, as here is where an incremental route to long-term physical as well as mental health begins.